Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine

iDMET: network-based approach for integrating differential analysis of cancer metabolomics

BACKGROUND

Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study.

RESULTS

We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered.

CONCLUSIONS

We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.

Emergence of infectious diseases and role of advanced nanomaterials in point-of-care diagnostics: a review

Infectious outbreaks are the foremost global public health concern, challenging the current healthcare system, which claims millions of lives annually. The most crucial way to control an infectious outbreak is by early detection through point-of-care (POC) diagnostics. POC diagnostics are highly advantageous owing to the prompt diagnosis, which is economical, simple and highly efficient with remote access capabilities. In particular, utilization of nanomaterials to architect POC devices has enabled highly integrated and portable (compact) devices with enhanced efficiency. As such, this review will detail the factors influencing the emergence of infectious diseases and methods for fast and accurate detection, thus elucidating the underlying factors of these infections. Furthermore, it comprehensively highlights the importance of different nanomaterials in POCs to detect nucleic acid, whole pathogens, proteins and antibody detection systems. Finally, we summarize findings reported on nanomaterials based on advanced POCs such as lab-on-chip, lab-on-disc-devices, point-of-action and hospital-on-chip. To this end, we discuss the challenges, potential solutions, prospects of integrating internet-of-things, artificial intelligence, 5G communications and data clouding to achieve intelligent POCs.

Intracellular and extracellular untargeted metabolomics reveal the effect of acute uranium exposure in HK-2 cells

Uranium exposure poses a serious threat to the health of occupational populations and the public. Although metabolomics is a promising research approach to study the toxicological mechanisms of uranium exposure, in vitro studies using human cells are scarce. Applying cultured cell metabolomics, we exhaustively analyzed the intracellular and extracellular differential metabolites upon uranium exposure and characterized the possible biological effects of uranium exposure on human kidney cells. Uranium exposure significantly induced disturbance in the amino acid biosynthesis and linoleic acid metabolism of the cells. Cells exposed to uranium produce excessive amounts of arachidonic acid, which has the potential to cause oxidative stress and damage cells. The results provide new evidence for an oxidative stress mechanism of uranium-induced renal cell injury. Cell metabolomics has proven to be a useful diagnostic tool to study the molecular mechanisms of uranium poisoning.

Inductively-Coupled Plasma Mass Spectrometry-Novel Insights From an Old Technology Into Stressed Red Blood Cell Physiology

BACKGROUND

Ion and metal homeostasis are critical to red blood cell physiology and Inductively Coupled Plasma (ICP) is a decades old approach to pursue elemental analysis. Recent evolution of ICP has resulted in its coupling to mass spectrometry (MS) instead of atomic absorption/emission.

METHODS

Here we performed Inductively-coupled plasma mass spectrometry (ICP-MS) measurements of intra- and extra-cellular Na, K, Ca, Mg, Fe, and Cu in red blood cells undergoing ionic, heat, or starvation stress. Results were correlated with Ca measurements from other common platforms (e.g., fluorescence-based approaches) and extensive measurements of red blood cell metabolism.

RESULTS

All stresses induced significant intra- and extracellular alterations of all measured elements. In particular, ionomycin treatment or hypertonic stress significantly impacted intracellular sodium and extracellular potassium and magnesium levels. Iron efflux was observed as a function of temperatures, with ionic and heat stress at 40°C causing the maximum decrease in intracellular iron pools and increases in the supernatants. Strong positive correlation was observed between calcium measurements via ICP-MS and fluorescence-based approaches. Correlation analyses with metabolomics data showed a strong positive association between extracellular calcium and intracellular sodium or magnesium levels and intracellular glycolysis. Extracellular potassium or iron were positively correlated with free fatty acids (especially mono-, poly-, and highly-unsaturated or odd-chain fatty acid products of lipid peroxidation). Intracellular iron was instead positively correlated with saturated fatty acids (palmitate, stearate) and negatively with methionine metabolism (methionine, S-adenosylmethionine), phosphatidylserine exposure and glycolysis.

CONCLUSION

In the era of omics approaches, ICP-MS affords a comprehensive characterization of intracellular elements that provide direct insights on red blood cell physiology and represent meaningful covariates for data generated via other omics platforms such as metabolomics.

A versatile and robust cell purification system with an RNA-only circuit composed of microRNA-responsive ON and OFF switches

Human induced pluripotent stem cells (iPSCs) are promising cell resources for cell therapy and drug discovery. However, iPSC-derived differentiated cells are often heterogenous and need purification using a flow cytometer, which has high cost and time consumption for large-scale purification. MicroRNAs (miRNAs) can be used as cell selection markers, because their activity differs between cell types. Here, we show miRNA-responsive ON and OFF switch mRNAs for robust cell purification. The ON switch contains a miRNA-target sequence after the polyadenylate tail, triggering translational activation by sensing the target miRNA. By designing RNA-only circuits with miRNA-ON and -OFF switch mRNAs that encode a lethal ribonuclease, Barnase, and its inhibitor, Barstar, we efficiently purified specific cell types, including human iPSCs and differentiated cardiomyocytes, without flow cytometry. Synthetic mRNA circuits composed of ON and OFF switches provide a safe, versatile, and time-saving method to purify various cell types for biological and clinical applications.

Defining the landscape of metabolic dysregulations in cancer metastasis

Metastasis is the primary cause of cancer related deaths due to the limited number of efficient druggable targets. Signatures of dysregulated cancer metabolism could serve as a roadmap for the determination of new treatment strategies. However, the metabolic signatures of metastatic cells remain vastly elusive. Our aim was to determine metabolic dysregulations associated with high metastatic potential in breast cancer cell lines. We have selected 5 triple negative breast cancer (TNBC) cell lines including three with high metastatic potential (HMP) (MDA-MB-231, MDA-MB-436, MDA-MB-468) and two with low metastatic potential (LMP) (BT549, HCC1143). The normal epithelial breast cell line (hTERT-HME1) was also investigated. The untargeted metabolic profiling of cells and growth media was conducted and total of 479 metabolites were quantified. First we characterized metabolic features differentiating TNBC cell lines from normal cells as well as identified cell line specific metabolic fingerprints. Next, we determined 92 metabolites in cells and 22 in growth medium that display significant differences between LMP and HMP. The HMP cell lines had elevated level of molecules involved in glycolysis, TCA cycle and lipid metabolism. We identified metabolic advantages of cell lines with HMP beyond enhanced glycolysis by pinpointing the role of branched chain amino acids (BCAA) catabolism as well as molecules supporting coagulation and platelet activation as important contributors to the metastatic cascade. The landscape of metabolic dysregulations, characterized in our study, could serve as a roadmap for the identification of treatment strategies targeting cancer cells with enhanced metastatic potential.

LC-MS Based Metabolomics Study of the Effects of EGCG on A549 Cells

(−)-Epigallocatechin-3-gallate (EGCG) is the main bioactive catechin in green tea. The antitumor activity of EGCG has been confirmed in various types of cancer, including lung cancer. However, the precise underlying mechanisms are still largely unclear. In the present study, we investigated the metabolite changes in A549 cells induced by EGCG in vitro utilizing liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. The result revealed 33 differentially expressed metabolites between untreated and 80 μM EGCG-treated A549 cells. The altered metabolites were involved in the metabolism of glucose, amino acid, nucleotide, glutathione, and vitamin. Two markedly altered pathways, including glycine, serine and threonine metabolism and alanine, aspartate and glutamate metabolism, were identified by MetaboAnalyst 5.0 metabolic pathway analysis. These results may provide potential clues for the intramolecular mechanisms of EGCG’s effect on A549 cells. Our study may contribute to future molecular mechanistic studies of EGCG and the therapeutic application of EGCG in cancer management.

A Metabolomics Investigation of the Metabolic Changes of Raji B Lymphoma Cells Undergoing Apoptosis Induced by Zinc Ions

Zinc plays a pivotal role in the function of cells and can induce apoptosis in various cancer cells, including Raji B lymphoma. However, the metabolic mechanism of Zn-induced apoptosis in Raji cells has not been explored. In this study, we performed global metabolic profiling using UPLC−Orbitrap−MS to assess the apoptosis of Raji cells induced by Zn ions released from ZnO nanorods. Multivariate analysis and database searches identified altered metabolites. Furthermore, the differences in the phosphorylation of 1380 proteins were also evaluated by Full Moon kinase array to discover the protein associated Zn−induced apoptosis. From the results, a prominent increase in glycerophosphocholine and fatty acids was observed after Zn ion treatment, but only arachidonic acid was shown to induce apoptosis. The kinase array revealed that the phosphorylation of p53, GTPase activation protein, CaMK2a, PPAR−γ, and PLA−2 was changed. From the pathway analysis, metabolic changes showed earlier onset than protein signaling, which were related to choline metabolism. LC−MS analysis was used to quantify the intracellular choline concentration, which decreased after Zn treatment, which may be related to the choline consumption required to produce choline-containing metabolites. Overall, we found that choline metabolism plays an important role in Zn-induced Raji cell apoptosis.

Effect of hypoxia factors gene silencing on ROS production and metabolic status of A375 malignant melanoma cells

The innate response of melanocytes to exogenous or endogenous stress stimuli like extreme pH and temperature, metabolite and oxygen deficiency or a high UV dose initiates a cellular stress response. This process activates adaptive processes to minimize the negative impact of the stressor on the pigment cell. Under physiological conditions, a non-cancer cell is directed to apoptosis if the stressor persists. However, malignant melanoma cells will survive persistent stress thanks to distinct "cancerous" signaling pathways (e.g. MEK) and transcription factors that regulate the expression of so-called "survival genes" (e.g. HIF, MITF). In this survival response of cancer cells, MEK pathway directs melanoma cells to deregulate mitochondrial metabolism, to accumulate reduced species (NADH), and to centralize metabolism in the cytosol. The aim of this work was to study the effect of gene silencing in malignant melanoma A375 cells on metabolic processes in cytosol and mitochondria. Gene silencing of HIF-1α, and miR-210 in normoxia and pseudohypoxia, and analysis of its effect on MITF-M, and PDHA1 expression. Detection of cytosolic NADH by Peredox-mCherry Assay. Detection of OCR, and ECAR using Seahorse XF96. Measurement of produced O₂^•- with MitoTracker Red CMXRos. ¹H NMR analysis of metabolites present in cell suspension, and medium. By gene silencing of HIF-1α and miR-210 the expression of PDHA1 was upregulated while that of MITF-M was downregulated, yielding acceleration of mitochondrial respiratory activity and thus elimination of ROS. Hence, we detected a significantly reduced A375 cell viability, an increase in alanine, inositol, nucleotides, and other metabolites that together define apoptosis. Based on the results of measurements of mitochondrial resipiratory activity, ROS production, and changes in the metabolites obtained in cells under the observed conditions, we concluded that silencing of HIF-1α and miR-210 yields apoptosis and, ultimately, apoptotic cell death in A375 melanoma cells.

Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues

The integration of untargeted metabolomics and transcriptomics from the same population of cells or tissue enhances the confidence in the identified metabolic pathways and understanding of the enzyme–metabolite relationship. Here, we optimised a simultaneous extraction method of metabolites/lipids and RNA from ependymoma cells (BXD-1425). Relative to established RNA (mirVana kit) or metabolite (sequential solvent addition and shaking) single extraction methods, four dual-extraction techniques were evaluated and compared (methanol:water:chloroform ratios): cryomill/mirVana (1:1:2); cryomill-wash/Econospin (5:1:2); rotation/phenol-chloroform (9:10:1); Sequential/mirVana (1:1:3). All methods extracted the same metabolites, yet rotation/phenol-chloroform did not extract lipids. Cryomill/mirVana and sequential/mirVana recovered the highest amounts of RNA, at 70 and 68% of that recovered with mirVana kit alone. sequential/mirVana, involving RNA extraction from the interphase of our established sequential solvent addition and shaking metabolomics-lipidomics extraction method, was the most efficient approach overall. Sequential/mirVana was applied to study a) the biological effect caused by acute serum starvation in BXD-1425 cells and b) primary ependymoma tumour tissue. We found (a) 64 differentially abundant metabolites and 28 differentially expressed metabolic genes, discovering four gene-metabolite interactions, and (b) all metabolites and 62% lipids were above the limit of detection, and RNA yield was sufficient for transcriptomics, in just 10 mg of tissue.

Phospholipid-Gold Nanorods Induce Energy Crisis in MCF-7 Cells: Cytotoxicity Evaluation Using LC-MS-Based Metabolomics Approach

Phospholipid-modified gold nanorods (phospholipid-GNRs) have demonstrated drastic cytotoxicity towards MCF-7 breast cancer cells compared to polyethylene glycol-coated GNRs (PEG-GNRs). In this study, the mechanism of cytotoxicity of phospholipid-GNRs towards MCF-7 cells was investigated using mass spectrometry-based global metabolic profiling and compared to PEGylated counterparts. The results showed that when compared to PEG-GNRs, phospholipid-GNRs induced significant and more pronounced impact on the metabolic profile of MCF-7 cells. Phospholipid-GNRs significantly decreased the levels of metabolic intermediates and end-products associated with cellular energy metabolisms resulting in dysfunction in TCA cycle, a reduction in glycolytic activity, and imbalance of the redox state. Additionally, phospholipid-GNRs disrupted several metabolism pathways essential for the normal growth and proliferation of cancer cells including impairment in purine, pyrimidine, and glutathione metabolisms accompanied by lower amino acid pools. On the other hand, the effects of PEG-GNRs were limited to alteration of glycolysis and pyrimidine metabolism. The current work shed light on the importance of metabolomics as a valuable analytical approach to explore the molecular effects of GNRs with different surface chemistry on cancer cell and highlights metabolic targets that might serve as promising treatment strategy in cancer.

MicroRNA-mediated stress response in bivalve species

Bivalve mollusks are important aquatic organisms, which are used for biological monitoring because of their abundance, ubiquitous nature, and abilities to adapt to different environments. MicroRNAs (miRNAs) are small noncoding RNAs, which typically silence the expression of target genes; however, certain miRNAs directly or indirectly upregulate their target genes. They are rapidly modulated and play an essential role in shaping the response of organisms to stresses. Based on the regulatory function and rapid alteration of miRNAs, they could act as biomarkers for biotic and abiotic stress, including environmental stresses and contaminations. Moreover, mollusk, particularly hemocytes, rapidly respond to environmental changes, such as pollution, salinity changes, and desiccation, which makes them an attractive model for this purpose. Thus, bivalve mollusks could be considered a good animal model to examine a system's response to different environmental conditions and stressors. miRNAs have been reported to adjust the adaptation and physiological functions of bivalves during endogenous and environmental stressors. In this review, we aimed to discuss the potential mechanisms underlying the response of bivalves to stressors and how miRNAs orchestrate this process; however, if necessary, other organisms' response is included to explain specific processes.

Decoding the metabolic landscape of pathophysiological stress-induced cell death in anucleate red blood cells

BACKGROUND

In response to stress, anucleate red blood cells (RBCs) can undergo a process of atypical cell death characterised by intracellular Ca²⁺ accumulation and phosphatidylserine (PS) externalisation. Here we studied alterations in RBC metabolism, a critical contributor to their capacity to survive environmental challenges, during this process.

MATERIALS AND METHODS

Metabolomics analyses of RBCs and supernatants, using ultra-high-pressure liquid chromatography coupled to mass spectrometry, were performed after in vitro exposure of RBCs to different pathophysiological cell stressors, including starvation, extracellular hypertonicity, hyperthermia, and supraphysiological ionic stress. Cell death was examined by flow cytometry.

RESULTS

Our data show that artificially enhancing RBC cytosolic Ca²⁺ influx significantly enhanced purine oxidation and strongly affected cellular bioenergetics by reducing glycolysis. Depleting extracellular Ca²⁺ curtailed starvation-induced cell death, an effect paralleled by the activation of compensatory pathways such as the pentose phosphate pathway, carboxylic acid metabolism, increased pyruvate to lactate ratios (methemoglobin reductase activation), one-carbon metabolism (protein-damage repair) and glutathione synthesis; RBCs exposed to hypertonic shock displayed a similar metabolic profile. Furthermore, cell stress promoted lipid remodelling as reflected by the levels of free fatty acids, acyl-carnitines and CoA precursors. Notably, RBC PS exposure, independently of the stressor, showed significant correlation with the levels of free fatty acids, glutamate, cystine, spermidine, tryptophan, 5-oxoproline, lactate, and hypoxanthine.

DISCUSSION

In conclusion, different cell death-inducing pathophysiological stressors, encountered in various clinical conditions, result in differential RBC metabolic phenotypes, only partly explained by intracellular Ca²⁺ levels and ATP availability.

Sperm-mediated DNA lesions alter metabolite levels in spent embryo culture medium

Paternal genetic alterations may affect embryo viability and reproductive outcomes. Currently it is unknown whether embryo metabolism is affected by sperm-mediated abnormalities. Hence, using a mouse model, this study investigated the response to paternally transmitted DNA lesions on genetic integrity and metabolism in preimplantation embryos. Spent embryo culture media were analysed for metabolites by nuclear magnetic resonance spectroscopy and embryonic genetic integrity was determined by terminal deoxynucleotidyl transferase dUTP nick-end labelling (TUNEL) assay on embryonic Day 4.5 (E4.5). Metabolic signatures were compared between normally derived embryos (control) and embryos derived from spermatozoa carrying induced DNA lesions (SDL). SDL embryos showed a significant reduction in blastocyst formation on E3.5 and E4.5 (P<0.0001) and had an approximately 2-fold increase in TUNEL-positive cells (P<0.01). A cohort of SDL embryos showing delayed development on E4.5 had increased uptake of pyruvate (P<0.05) and released significantly less alanine (P<0.05) to the medium compared with the corresponding control embryos. On the other hand, normally developed SDL embryos had a reduced (P<0.001) pyruvate-to-alanine ratio compared with normally developed embryos from the control group. Hence, the difference in the metabolic behaviour of SDL embryos may be attributed to paternally transmitted DNA lesions in SDL embryos.

LC-MS metabolomics comparisons of cancer cell and macrophage responses to methotrexate and polymer-encapsulated methotrexate

Methotrexate (MTX) is a folate analogue antimetabolite widely used for the treatment of rheumatoid arthritis and cancer. A number of studies have shown that MTX delivered via nanoparticle carriers is more potent against cancer cells than free MTX, a phenomenon attributed to higher cellular uptake of the particles compared to the saturable folate receptor pathway. In this study, a cell-based global metabolic profiling approach was applied to study the effects of MTX in both free drug form and when encapsulated in -poly(lactide-co-glycolide) (PLGA) nanoparticles on a cancer cell line, A549, and also on human-like THP-1 macrophages. The results showed that MTX loaded nanoparticles had less impact on the macrophages than free MTX, and the effects on macrophages were limited to changes in nucleotide metabolism and suppression of the tricarboxylic acid cycle, whereas free MTX also led to a drop in glycolytic activity and impairment in redox homeostasis. In contrast, MTX loaded nanoparticles showed a greater impact on A549 cells than the free drug, which was in accord with studies in other cell lines in prior literature with MTX-carrier nanoparticles.

Mass spectrometry-based urinary metabolomics for the investigation on the mechanism of action of Eleutherococcus senticosus (Rupr. & Maxim.) Maxim. leaves against ischemic stroke in rats

ETHNOPHARMACOLOGICAL RELEVANCE

As a traditional Chinese medicine, Eleutherococcus senticosus (Rupr. & Maxim.) Maxim. leaves (ESL) can treat ischemic, neurasthenia, and hypertension diseases. However, only few studies have been conducted on the mechanism of action of ESL for ischemic disease treatment.

AIM OF THE STUDY

This study aimed to discover the potential biomarkers in the rats caused by ischemic stroke and build a gene-enzyme-biomarker network to explore the mechanism of ESL treatment on ischemic stroke further.

MATERIALS AND METHODS

The urinary metabolomics strategy was developed by combining UPLC-Q-TOF/MS with multivariate data analysis. The gene-enzyme-biomarker network was built by Cytoscape 3.6.0 on the basis of the potential biomarkers filtered out via urinary metabolomic analysis. Then, the potential target enzymes of ESL in the treatment of ischemic stroke were selected for further validation analysis via the ELISA kits.

RESULTS

A total of 42 biomarkers associated with ischemic stroke have been identified, among which 38 species can be adjusted by ESL, including 5'-methylthioadenosine, prostaglandin A2, l-methionine, aldosterone, 11b-hydroxyprogesterone, prostaglandin E3, dehydroepiandrosterone, taurine, 5-methoxyindoleacetate, and p-cresol glucuronide. These biomarkers were involved in several metabolic pathways, including taurine and hypotaurine, arachidonic acid, cysteine and methionine, steroid hormone biosynthesis, tryptophan, and tyrosine metabolism pathways. The gene-enzyme-biomarker network was built, and three predicted target proteins, including cyclooxygenase-2 (COX-2), monoamine oxidase (MAO), and nitric oxide synthase (NOS), were selected as the potential target enzymes for ESL in ischemic stroke treatment.

CONCLUSIONS

All results showed that ESL can play a therapeutic role in treating ischemic stroke through different pathways. This study will provide an overall view of the mechanism underlying the action of ESL against ischemic stroke.

Male-biased zebrafish sex differentiation and metabolomics profile changes caused by dydrogesterone

Some progestins, including the widely used dydrogesterone (DDG), have been shown to cause male-biased sex ratio in teleost. However, there is a gap to fully understand the mechanisms of the sex differentiation disturbance by progestins, particularly from the metabolic aspect. We thus aimed to examine the sex changes by exposing zebrafish embryos to 4.4 (L), 44 (M) and 440 (H) ng/L DDG for up to 140 days, and investigated metabolomic profile changes during the critical period of sex differentiation at fry stage (35 dpf). DDG increased the percentage of male zebrafish in a dose-dependent manner, with 98% male fish in the high concentration group. In zebrafish fry, DDG increased the levels of some free fatty acids, monoglycerides, acylcarnitines, organic acids, free amino acids, while decreased lysophospholipids, uric acid and bile acids. DDG exposure also decreased the nucleoside monophosphates and UDP-sugars while increased nucleosides and their bases. These metabolite changes, namely increase in n-3 PUFAs (polyunsaturated fatty acids), myo-inositol, taurine, palmitoleic acid, oleic acid, lactic acid, fumaric acid, and uracil, and decrease in uric acid and bile acids, might account for the male-biased sex ratio in zebrafish. It appears that many of these metabolites could inhibit several pathways that regulate zebrafish gonad differentiation, including NF-κB/COX-2 and Wnt/β-catenin pathways, and activate p53 pathway. Thus we proposed a hypothesis that DDG might induce oocytes apoptosis through the above pathways and finally lead to female-to-male sex reversal. The results from this study suggest that DDG at environmentally relevant concentrations could affect zebrafish metabolomic profiles and finally disturb fish sex differentiation.

Copper-induced immunomodulation in mussel (Perna canaliculus) haemocytes

Copper is a common contaminant in aquatic environments, which may cause physiological dysfunction in marine organisms. However, the toxicity mechanisms of copper in marine bivalves is not fully understood. In this study, we applied an integrated approach that combines flow cytometry and Gas Chromatography-Mass Spectrometry (GC-MS)-based metabolomics to characterize cellular and molecular mechanisms of copper immunotoxicity in New Zealand Greenshell™ mussel (Perna canaliculus) haemolymph. Flow cytometric results showed significant increases in haemocyte mortality, production of reactive oxygen species and apoptosis (via alteration of caspase 3/7 and mitochondrial membrane potential) of haemocytes exposed to increasing total concentrations of Cu2+ (62.5, 125.0 and 187.5 μM) compared to a low Cu2+ concentration (25.0 μM) and control (0.0 μM). In addition to flow cytometric data, our metabolomics results showed alterations of 25 metabolites within the metabolite profile of Cu2+-exposed haemolymph (125 μM) compared to those of control samples. Changes in levels of these metabolites may be considered important signatures of oxidative stress (e.g., glutathione) and apoptosis processes (e.g., alanine, glutamic acid). This study provides insights into the cellular and molecular mechanisms of oxidative stress and apoptosis in marine bivalves and highlights the applicability and reliability of metabolomic techniques for immunotoxicological studies in marine organisms.

Investigating the intracellular effects of hyperbranched polycation-DNA complexes on lung cancer cells using LC-MS-based metabolite profiling

Cationic polymers have emerged as a promising alternative to viral vectors in gene therapy. They are cheap to scale up, easy to functionalise and are potentially safer than viral vectors, however many are cytotoxic. The large number of polycations, designed to address the toxicity problem, raises a practical need to develop a fast and reliable method for assessing the safety of these materials. In this regard, metabolomics provides a detailed and comprehensive method that can assess the potential toxicity at the cellular and molecular level. Here, we applied metabolomics to investigate the impact of hyperbranched polylysine, hyperbranched polylysine-co-histidine and branched polyethyleneimine polyplexes at sub-toxic concentrations on the metabolic pathways of A459 and H1299 lung carcinoma cell lines. The study revealed that the polyplexes downregulated metabolites associated with glycolysis and the TCA cycle, and induced oxidative stress in both cell lines. The relative changes of the metabolites indicated that the polyplexes of polyethyleneimine and hyperbranched polylysine affected the metabolism much more than the polyplexes of hyperbranched polylysine-co-histidine. This was in line with transfection results, suggesting a correlation between the toxicity and transfection efficiency of these polyplexes. Our work highlights the importance of the metabolomics approach not just to assess the potential toxicity of polyplexes but also to understand the molecular mechanisms underlying any adverse effects, which could help in designing more efficient vectors.

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

Investigating the deregulation of metabolic tasks via Minimum Network Enrichment Analysis (MiNEA) as applied to nonalcoholic fatty liver disease using mouse and human omics data

Nonalcoholic fatty liver disease (NAFLD) is associated with metabolic syndromes spanning a wide spectrum of diseases, from simple steatosis to the more complex nonalcoholic steatohepatitis. To identify the deregulation that occurs in metabolic processes at the molecular level that give rise to these various NAFLD phenotypes, algorithms such as pathway enrichment analysis (PEA) can be used. These analyses require the use of predefined pathway maps, which are composed of reactions describing metabolic processes/subsystems. Unfortunately, the annotation of the metabolic subsystems can differ depending on the pathway database used, making these approaches subject to biases associated with different pathway annotations, and these methods cannot capture the balancing of cofactors and byproducts through the complex nature and interactions of genome-scale metabolic networks (GEMs). Here, we introduce a framework entitled Minimum Network Enrichment Analysis (MiNEA) that is applied to GEMs to generate all possible alternative minimal networks (MiNs), which are possible and feasible networks composed of all the reactions pertaining to various metabolic subsystems that can synthesize a target metabolite. We applied MiNEA to investigate deregulated MiNs and to identify key regulators in different NAFLD phenotypes, such as a fatty liver and liver inflammation, in both humans and mice by integrating condition-specific transcriptomics data from liver samples. We identified key deregulations in the synthesis of cholesteryl esters, cholesterol, and hexadecanoate in both humans and mice, and we found that key regulators of the hydrogen peroxide synthesis network were regulated differently in humans and mice. We further identified which MiNs demonstrate the general and specific characteristics of the different NAFLD phenotypes. MiNEA is applicable to any GEM and to any desired target metabolite, making MiNEA flexible enough to study condition-specific metabolism for any given disease or organism.

This work aims to introduce a network-based enrichment analysis using metabolic networks and transcriptomics data. Previous pathways/subsystems enrichment methods use predefined gene annotations of metabolic processes and gene annotations can differ based on different resources and can produce bias in pathway definitions. Thus, we introduce a framework, Minimum Network Enrichment Analysis (MiNEA), which first finds all possible minimal-size networks for a given metabolic process/task and then identifies deregulated minimal networks using deregulated genes between two conditions. MiNEA also identifies the deregulation in key reactions that are overlapped across all possible minimal-size networks. We applied MiNEA to identify deregulated metabolic tasks and their synthesis networks in the steatosis and the nonalcoholic steatohepatitis (NASH) diseases using a metabolic network and transcriptomics data of mouse and human liver samples. We identified key regulators of NASH for the synthesis networks of hydrogen peroxide and ceramide in both humans and mice. We also identified opposite deregulation in NASH for the phosphatidylserine synthesis network between humans and mice. MiNEA finds synthesis networks for a given target metabolite and due to this it is flexible to study deregulation in different phenotypes. MiNEA can be widely applicable for studying context-specific metabolism for any organism because the metabolic networks and context-specific gene expression data are now available for many organisms.

Urea influences amino acid turnover in bovine cumulus-oocyte complexes, cumulus cells and denuded oocytes, and affects in vitro fertilization outcome

High-protein diets often lead to an increase in urea concentration in follicular fluid of dairy cows, which may reduce oocyte competence. In the present study, maturation media were supplemented with urea (0, 20, 40 mg/dl), and amino acids (AAs) turnover was evaluated in the 24-h spent media of specimens (cell types), bovine cumulus-oocyte complexes (COCs), cumulus cells (CCs), or denuded oocytes (DOs). The main effects of urea and cell type, and their interaction were significant on the individual turnover (expect threonine, glycine, and tyrosine) and total turnover, depletion, and appearance of AAs. The results showed a high level of urea and DOs increased the depletion of all AAs and that of essential and non-AAs, respectively. Sensitivity analysis revealed the highest sensitivity of isoleucine, lysine, and tryptophan to urea, especially in DOs. Principal component analysis (PCA) evaluated the strong correlations between the turnover of: (1) glutamine, aspartic acid or glycine, and developmental competence and fertilization of COCs; (2) serine, isoleucine, valine or glutamic acid, and cleavage rate of DOs; and (3) serine, glutamine, aspartic acid or alanine, and CCs viability. In conclusion, urea significantly changed the turnover of AAs by COCs, CCs and DOs, and reduced the subsequent developmental competence of bovine oocytes.

IntLIM: integration using linear models of metabolomics and gene expression data

BACKGROUND

Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites.

RESULTS

The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally.

CONCLUSIONS

IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

Experimental design and reporting standards for metabolomics studies of mammalian cell lines

Metabolomics is an analytical technique that investigates the small biochemical molecules present within a biological sample isolated from a plant, animal, or cultured cells. It can be an extremely powerful tool in elucidating the specific metabolic changes within a biological system in response to an environmental challenge such as disease, infection, drugs, or toxins. A historically difficult step in the metabolomics pipeline is in data interpretation to a meaningful biological context, for such high-variability biological samples and in untargeted metabolomics studies that are hypothesis-generating by design. One way to achieve stronger biological context of metabolomic data is via the use of cultured cell models, particularly for mammalian biological systems. The benefits of in vitro metabolomics include a much greater control of external variables and no ethical concerns. The current concerns are with inconsistencies in experimental procedures and level of reporting standards between different studies. This review discusses some of these discrepancies between recent studies, such as metabolite extraction and data normalisation. The aim of this review is to highlight the importance of a standardised experimental approach to any cultured cell metabolomics study and suggests an example procedure fully inclusive of information that should be disclosed in regard to the cell type/s used and their culture conditions. Metabolomics of cultured cells has the potential to uncover previously unknown information about cell biology, functions and response mechanisms, and so the accurate biological interpretation of the data produced and its ability to be compared to other studies should be considered vitally important.

Monitoring for Response to Antineoplastic Drugs: The Potential of a Metabolomic Approach

For most cancers, chemotherapeutic options are rapidly expanding, providing the oncologist with substantial choices. Therefore, there is a growing need to select the best systemic therapy, for any individual, that effectively halts tumor progression with minimal toxicity. Having the capability to predict benefit and to anticipate toxicity would be ideal, but remains elusive at this time. An alternative approach is an adaptive approach that involves close observation for treatment response and emergence of resistance. Currently, response to systemic therapy is estimated using radiographic tests. Unfortunately, radiographic estimates of response are imperfect and radiographic signs of response can be delayed. This is particularly problematic for targeted agents, as tumor shrinkage is often not apparent with these drugs. As a result, patients are exposed to prolonged courses of toxic drugs that may ultimately be found to be ineffective. A biomarker-based adaptive strategy that involves the serial analysis of the metabolome is attractive. The metabolome changes rapidly with changes in physiology. Changes in the circulating metabolome associated with various antineoplastic agents have been described, but further work will be required to understand what changes signify clinical benefit. We present an investigative approach for the discovery and validation of metabolomic response biomarkers, which consists of serial analysis of the metabolome and linkage of changes in the metabolome to measurable therapeutic benefit. Potential pitfalls in the development of metabolomic biomarkers of response and loss of response are reviewed.

Multi-omics analysis reveals that ornithine decarboxylase contributes to erlotinib resistance in pancreatic cancer cells

Molecular and metabolic alterations in cancer cells are one of the leading causes of acquired resistance to chemotherapeutics. In this study, we explored an experimental strategy to identify which of these alterations can induce erlotinib resistance in human pancreatic cancer. Using genetically matched erlotinib-sensitive (BxPC-3) and erlotinib-resistant (BxPC-3ER) pancreatic cancer cells, we conducted a multi-omics analysis of metabolomes and transcriptomes in these cells. Untargeted and targeted metabolomic analyses revealed significant changes in metabolic pathways involved in the regulation of polyamines, amino acids, and fatty acids. Further transcriptomic analysis identified that ornithine decarboxylase (ODC) and its major metabolite, putrescine, contribute to the acquisition of erlotinib resistance in BxPC-3ER cells. Notably, either pharmacological or genetic blockage of ODC was able to restore erlotinib sensitivity, and this could be rescued by treatment with exogenous putrescine in erlotinib-resistant BxPC-3ER cells. Moreover, using a panel of cancer cells we demonstrated that ODC expression levels in cancer cells are inversely correlated with sensitivity to chemotherapeutics. Taken together, our findings will begin to uncover mechanisms of acquired drug resistance and ultimately help to identify potential therapeutic markers in cancer.

Metabolomics-Proteomics Combined Approach Identifies Differential Metabolism-Associated Molecular Events between Senescence and Apoptosis

Apoptosis and senescence are two types of cell fates in response to chemotherapy. Besides canonical pathways that mediate cell fates, cancer cell metabolism has been revealed as a crucial factor affecting cell fate decisions and thus represents a new target for antitumor therapy. Therefore, a comprehensive description of metabolic pathways underlying cell senescence and apoptosis in response to chemotherapy is highly demanded for therapeutic exploitation of both processes. Herein we employed a metabolomics-proteomics combined approach to identify metabolism-associated molecular events that mediate cellular responses to senescence and apoptosis using doxorubicin-treated human breast cancer cells MCF7 as models. Such biomics approach revealed that tricarboxylic acid cycle, pentose phosphate pathway, and nucleotide synthesis pathways were significantly upregulated in the senescent model, whereas fatty acid synthesis was reduced. In apoptotic cells, an overall reduced activity of major metabolic pathways was observed except for the arginine and proline pathway. Combinatorially, these data show the utility of biomics in exploring biochemical mechanism-based differences between apoptosis and senescence and reveal an unprecedented finding of the metabolic events that were induced for survival by facilitating ROS elimination and DNA damage repair in senescent cells, while they were downregulated in apoptotic cells when DNA damage was irreparable.

Metabolic characterization of invaded cells of the pancreatic cancer cell line, PANC-1

We previously reported that about 0.4% of cells in the cultured human pancreatic cancer cell line, PANC-1, can invade matrigel during the transwell invasion assay, suggesting that these invaded PANC-1 cells may have specific characteristics to keep their invasive potential. To identify the metabolic characterization specific in the invaded PANC-1 cells, metabolome analysis of the invaded PANC-1 compared with the whole cultured PANC-1 was performed using CE-TOFMS, and concentrations of 110 metabolites were measured. In contrast to the whole cultured cells, the invaded PANC-1 was characterized as a population with reduced levels of amino acids and TCA cycle intermediates, and decreased and increased intermediates in glycolysis and nucleic acid metabolism. In particular, the ratio of both adenosine and guanosine energy charge was reduced in the invaded cells, revealing that the consumption of ATP and GTP was high in the invaded cells, and thus suggesting that ATP- or GTP-generating pathways are stimulated. In addition, the GSH/GSSG ratio was low in the invaded cells, but these cells had a higher surviving fraction after exposure to hydrogen peroxide. Thus, the invaded cells were the population resistant to oxidative stress. Furthermore, reduction in intracellular GSH content inhibited PANC-1 invasiveness, indicated that GSH has an important role in PANC-1 invasiveness. Overall, we propose the invaded cells have several unique metabolic profiles.

Discovery of Metabolite Biomarkers for Acute Ischemic Stroke Progression

Stroke remains a major public health problem worldwide; it causes severe disability and is associated with high mortality rates. However, early diagnosis of stroke is difficult, and no reliable biomarkers are currently established. In this study, mass-spectrometry-based metabolomics was utilized to characterize the metabolic features of the serum of patients with acute ischemic stroke (AIS) to identify novel sensitive biomarkers for diagnosis and progression. First, global metabolic profiling was performed on a training set of 80 human serum samples (40 cases and 40 controls). The metabolic profiling identified significant alterations in a series of 26 metabolites with related metabolic pathways involving amino acid, fatty acid, phospholipid, and choline metabolism. Subsequently, multiple algorithms were run on a test set consisting of 49 serum samples (26 cases and 23 controls) to develop different classifiers for verifying and evaluating potential biomarkers. Finally, a panel of five differential metabolites, including serine, isoleucine, betaine, PC(5:0/5:0), and LysoPE(18:2), exhibited potential to differentiate AIS samples from healthy control samples, with area under the receiver operating characteristic curve values of 0.988 and 0.971 in the training and test sets, respectively. These findings provided insights for the development of new diagnostic tests and therapeutic approaches for AIS.

Change of the metabolomic profile during short-term mononuclear cell storage

BACKGROUND AND OBJECTIVES

Short-term storage of leukapheresis products used for immunotherapeutic mononuclear cell (MNC) products is a frequent event. The analysis of time-related metabolic patterns enables the characterization of storage-related effects in MNCs and the hypothesis-based optimization of the MNC medium.

MATERIALS AND METHODS

The MNC products from seven leukapheresis procedures were stored within a closed bag system for 48 h. Concentrations of amino acids, biogenic amines, phospho- and sphingolipids and hexoses in the medium were measured by targeted metabolomics. The viability of MNC subpopulations was assayed by Annexin V (AnV) and JC-1 staining.

RESULTS

Glucose depletion and a significant change of the acylcarnitine profile are early events within the first 24 h of storage. In contrast, for most amino acids, the maximum increase was observed at 48 h of storage as mirrored by an increase in the amino acid levels by a mean factor of 1·2 (1·3, 2·0) after 6 h (24 h, 48 h, respectively). This was except for the concentrations of glutamine and lysine, which did not change significantly. The taurine concentration showed a twofold increase within the first 24 h and remained constant thereafter. The steepest increase in AnV⁺ and 7-AAD⁺ CD4⁺ T cells was found between 24 and 48 h.

CONCLUSION

The time-course of apoptosis and metabolic patterns in the MNC products demonstrate that 24 h of storage is a decisive time-point, as afterwards key metabolic pathways showed nonlinear detrimental changes. Optimization of storage by supplementation of specific substrates demands therefore an early intervention.

Programmed cell death in periodontitis: recent advances and future perspectives

Periodontitis is a highly prevalent infectious disease, characterized by destruction of the periodontium, and is the main cause of tooth loss. Periodontitis is initiated by periodontal pathogens, while other risk factors including smoking, stress, and systemic diseases aggravate its progression. Periodontitis affects many people worldwide, but the molecular mechanisms by which pathogens and risk factors destroy the periodontium are unclear. Programmed cell death (PCD), different from necrosis, is an active cell death mediated by a cascade of gene expression events and can be mainly classified into apoptosis, autophagy, necroptosis, and pyroptosis. Although PCD is involved in many inflammatory diseases, its correlation with periodontitis is unclear. After reviewing the relevant published articles, we found that apoptosis has indeed been reported to play a role in periodontitis. However, the role of autophagy in periodontitis needs further verification. Additionally, implication of necroptosis or pyroptosis in periodontitis remains unknown. Therefore, we recommend future studies, which will unravel the pivotal role of PCD in periodontitis, allowing us to prevent, diagnose, and treat the disease, as well as predict its outcomes.

Urine metabolomic profiling of children with respiratory tract infections in the emergency department: a pilot study

BACKGROUND

Clinicians lack objective tests to help determine the severity of bronchiolitis or to distinguish a viral from bacterial causes of respiratory distress. We hypothesized that children with respiratory syncytial virus (RSV) infection would have a different metabolomic profile compared to those with bacterial infection or healthy controls, and this might also vary with bronchiolitis severity.

METHODS

Clinical information and urine-based metabolomic data were collected from healthy age-matched children (n = 37) and those admitted to hospital with a proven infection (RSV n = 55; Non-RSV viral n = 16; bacterial n = 24). Nuclear magnetic resonance (NMR) measured 86 metabolites per urine sample. Partial least squares discriminant analysis (PLS-DA) was performed to create models of separation.

RESULTS

Using a combination of metabolites, a strong PLS-DA model (R2 = 0.86, Q2 = 0.76) was created differentiating healthy children from those with RSV infection. This model had over 90 % accuracy in classifying blinded infants with similar illness severity. Two other models differentiated length of hospitalization and viral versus bacterial infection.

CONCLUSION

While the sample sizes remain small, this is the first report suggesting that metabolomic analysis of urine samples has the potential to become a diagnostic aid. Future studies with larger sample sizes are required to validate the utility of metabolomics in pediatric patients with respiratory distress.

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.

Metabolomics identifies the intersection of phosphoethanolamine with menaquinone-triggered apoptosis in an in vitro model of leukemia

Altered metabolism is increasingly acknowledged as an important aspect of cancer, and thus serves as a potentially fertile area for the identification of therapeutic targets or leads. Our recent work using transcriptional data to predict metabolite levels in cancer cells led to preliminary evidence of the antiproliferative role of menaquinone (vitamin K2) in the Jurkat cell line model of acute lymphoblastic leukemia. However, nothing is known about the direct metabolic impacts of menaquinone in cancer, which could provide insights into its mechanism of action. Here, we used metabolomics to investigate the process by which menaquinone exerts antiproliferative activity on Jurkat cells. We first validated the dose-dependent, semi-selective, pro-apoptotic activity of menaquinone treatment on Jurkat cells relative to non-cancerous lymphoblasts. We then used mass spectrometry-based metabolomics to identify systems-scale changes in metabolic dynamics that are distinct from changes induced in non-cancerous cells or by other chemotherapeutics. One of the most significantly affected metabolites was phosphoethanolamine, which exhibited a two-fold increase in menaquinone-treated Jurkat cells compared to vehicle-treated cells at 24 h, growing to a five-fold increase at 72 h. Phosphoethanolamine elevation was observed prior to the induction of apoptosis, and was not observed in menaquinone-treated lymphoblasts or chemotherapeutic-treated Jurkat cells. We also validated the link between menaquinone and phosphoethanolamine in an ovarian cancer cell line, suggesting potentially broad applicability of their relationship. This metabolomics-based work is the first detailed characterization of the metabolic impacts of menaquinone treatment and the first identified link between phosphoethanolamine and menaquinone-induced apoptosis.

Distinct Metabolic Signature of Human Bladder Cancer Cells Carrying an Impaired Fanconi Anemia Tumor-Suppressor Signaling Pathway

Metabolic profiling has great potential to help the diagnosis and prognosis of cancer patients. Fanconi Anemia (FA) tumor-suppressor signaling has been instrumental in understanding human tumorigenesis. However, this instrumental understanding has never been demonstrated at the metabolic level. Here, we show that impaired FA signaling can lead cells to exhibit metabolic signatures of tumorigenesis. This is consistent with our original studies of the roles of FA signaling in suppressing non-FA tumorigenesis at functional and genetic levels. Using ultraperformance liquid chromatography-mass spectroscopy and gas chromatography-mass spectrometry, we characterized metabolic alterations in bladder cancer cells carrying an intact or impaired FA pathway. The latter was obtained by ectopically expressing FAVL (FAVL-high), which we previously found to be capable of inactivating FA signaling. A total of 18 metabolites, end products of cell proliferation or apoptosis, were significantly different between FAVL-high and -low cells. Methionine, phenylalanine, and threonine, resulting from a tumorigenic process, were substantially increased in FAVL-high cells. With this study, we achieved genomic, functional, and metabolomic characterization of the roles of FA signaling in the development of human cancer. Furthermore, this study provides novel insights into how to translate FA basic research into strategies for producing effective biomarkers in human cancer diagnosis and prognosis.

Globally Optimized Targeted Mass Spectrometry: Reliable Metabolomics Analysis with Broad Coverage

Targeted detection is one of the most important methods in mass spectrometry (MS)-based metabolomics; however, its major limitation is the reduced metabolome coverage that results from the limited set of targeted metabolites typically used in the analysis. In this study we describe a new approach, globally optimized targeted (GOT)-MS, that combines many of the advantages of targeted detection and global profiling in metabolomics analysis, including the capability to detect unknowns, broad metabolite coverage, and excellent quantitation. The key step in GOT-MS is a global search of precursor and product ions using a single liquid chromatography-triple quadrupole (LC-QQQ) mass spectrometer. Here, focused on measuring serum metabolites, we obtained 595 precursor ions and 1 890 multiple reaction monitoring (MRM) transitions, under positive and negative ionization modes in the mass range of 60-600 Da. For many of the MRMs/metabolites under investigation, the analytical performance of GOT-MS is better than or at least comparable to that obtained by global profiling using a quadrupole-time-of-flight (Q-TOF) instrument of similar vintage. Using a study of serum metabolites in colorectal cancer (CRC) as a representative example, GOT-MS significantly outperformed a large targeted MS assay containing ∼160 biologically important metabolites and provided a complementary approach to traditional global profiling using Q-TOF-MS. GOT-MS thus expands and optimizes the detection capabilities for QQQ-MS through a novel approach and should have the potential to significantly advance both basic and clinical metabolic research.

Blood transcriptomics and metabolomics for personalized medicine

Molecular analysis of blood samples is pivotal to clinical diagnosis and has been intensively investigated since the rise of systems biology. Recent developments have opened new opportunities to utilize transcriptomics and metabolomics for personalized and precision medicine. Efforts from human immunology have infused into this area exquisite characterizations of subpopulations of blood cells. It is now possible to infer from blood transcriptomics, with fine accuracy, the contribution of immune activation and of cell subpopulations. In parallel, high-resolution mass spectrometry has brought revolutionary analytical capability, detecting > 10,000 metabolites, together with environmental exposure, dietary intake, microbial activity, and pharmaceutical drugs. Thus, the re-examination of blood chemicals by metabolomics is in order. Transcriptomics and metabolomics can be integrated to provide a more comprehensive understanding of the human biological states. We will review these new data and methods and discuss how they can contribute to personalized medicine.

Metabolic signatures differentiate ovarian from colon cancer cell lines

Background

In this era of precision medicine, the deep and comprehensive characterization of tumor phenotypes will lead to therapeutic strategies beyond classical factors such as primary sites or anatomical staging. Recently, “-omics” approached have enlightened our knowledge of tumor biology. Such approaches have been extensively implemented in order to provide biomarkers for monitoring of the disease as well as to improve readouts of therapeutic impact. The application of metabolomics to the study of cancer is especially beneficial, since it reflects the biochemical consequences of many cancer type-specific pathophysiological processes. Here, we characterize metabolic profiles of colon and ovarian cancer cell lines to provide broader insight into differentiating metabolic processes for prospective drug development and clinical screening.

Methods

We applied non-targeted metabolomics-based mass spectroscopy combined with ultrahigh-performance liquid chromatography and gas chromatography for the metabolic phenotyping of four cancer cell lines: two from colon cancer (HCT15, HCT116) and two from ovarian cancer (OVCAR3, SKOV3). We used the MetaP server for statistical data analysis.

Results

A total of 225 metabolites were detected in all four cell lines; 67 of these molecules significantly discriminated colon cancer from ovarian cancer cells. Metabolic signatures revealed in our study suggest elevated tricarboxylic acid cycle and lipid metabolism in ovarian cancer cell lines, as well as increased β-oxidation and urea cycle metabolism in colon cancer cell lines.

Conclusions

Our study provides a panel of distinct metabolic fingerprints between colon and ovarian cancer cell lines. These may serve as potential drug targets, and now can be evaluated further in primary cells, biofluids, and tissue samples for biomarker purposes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0576-z) contains supplementary material, which is available to authorized users.

Fatal hyperammonemia and carbamoyl phosphate synthetase 1 (CPS1) deficiency following high-dose chemotherapy and autologous hematopoietic stem cell transplantation

Fatal hyperammonemia secondary to chemotherapy for hematological malignancies or following bone marrow transplantation has been described in few patients so far. In these, the pathogenesis of hyperammonemia remained unclear and was suggested to be multifactorial. We observed severe hyperammonemia (maximum 475 μmol/L) in a 2-year-old male patient, who underwent high-dose chemotherapy with carboplatin, etoposide and melphalan, and autologous hematopoietic stem cell transplantation for a neuroblastoma stage IV. Despite intensive care treatment, hyperammonemia persisted and the patient died due to cerebral edema. The biochemical profile with elevations of ammonia and glutamine (maximum 1757 μmol/L) suggested urea cycle dysfunction. In liver homogenates, enzymatic activity and protein expression of the urea cycle enzyme carbamoyl phosphate synthetase 1 (CPS1) were virtually absent. However, no mutation was found in CPS1 cDNA from liver and CPS1 mRNA expression was only slightly decreased. We therefore hypothesized that the acute onset of hyperammonemia was due to an acquired, chemotherapy-induced (posttranscriptional) CPS1 deficiency. This was further supported by in vitro experiments in HepG2 cells treated with carboplatin and etoposide showing a dose-dependent decrease in CPS1 protein expression. Due to severe hyperlactatemia, we analysed oxidative phosphorylation complexes in liver tissue and found reduced activities of complexes I and V, which suggested a more general mitochondrial dysfunction. This study adds to the understanding of chemotherapy-induced hyperammonemia as drug-induced CPS1 deficiency is suggested. Moreover, we highlight the need for urgent diagnostic and therapeutic strategies addressing a possible secondary urea cycle failure in future patients with hyperammonemia during chemotherapy and stem cell transplantation.

Enhancing metabolomics research through data mining

Metabolomics research, like other disciplines utilizing high-throughput technologies, generates a large amount of data for every sample. Although handling this data is a challenge and one of the biggest bottlenecks of the metabolomics workflow, it is also the clue to accomplish valuable results. This work has been designed to supply methodological data mining guidelines, describing systematically the steps to be followed in metabolomics data exploration. Instrumental raw data refinement in the pre-processing step and assessment of the statistical assumptions in pre-treatment directly affect the results of subsequent univariate and multivariate analyses. A study of aging in a healthy population was selected to represent this data mining process. Multivariate analysis of variance and linear regression methods were used to analyze the metabolic changes underlying aging. Selection of both multivariate methods aims to illustrate the treatment of age from two rather different perspectives, as a categorical variable and a continuous variable.

BIOLOGICAL SIGNIFICANCE

Metabolomics is a discipline involving the analysis of a large amount of data to gather relevant information. Researchers in this field have to overcome the challenges of complex data processing and statistical analysis issues. A wide range of tasks has to be executed, from the minimization of batch-to-batch/systematic variations in pre-processing, to the application of common data analysis techniques relying on statistical assumptions. In this work, a real-data metabolic profiling research on aging was used to illustrate the proposed workflow and suggest a set of guidelines for analyzing metabolomics data. This article is part of a Special Issue entitled: HUPO 2014.

Perioperative dynamics and significance of amino acid profiles in patients with cancer

Background

Metabolome analysis including amino acid profile is under investigation as an approach in cancer screening. The present study aims to analyze plasma free amino acid (PFAA) profiles in cancer patients and investigate their potential as biomarkers of malignancy.

Methods

Plasma samples from 56 gastric cancer patients, 28 breast cancer patients, 33 thyroid cancer patients, and 137 age-matched healthy controls were collected in the study. PFAA levels were measured and their perioperative alterations were analyzed. Biological effects of ten cancer-related amino acids were further validated in gastric and breast cancer cells.

Results

We found that PFAA profiles of cancer patients differed significantly from those of healthy controls. Decreased concentrations of PFAAs were associated with lymph node metastases in gastric cancer. Levels of PFAAs such as aspartate and alanine increased after tumor resection. PFAA levels correlated with clinical tumor markers in gastric cancer patients and pathological immunohistochemistry markers in breast cancer patients. Specifically, alanine, arginine, aspartate and cysteine had proliferative effects on breast cancer cells. Proliferation of gastric cancer cells was promoted by cysteine, but inhibited by alanine and glutamic acid. Furthermore, alanine treatment decreased total and stable fraction of gastric cancer cells, and alanine and glutamic acid induced apoptosis of gastric cancer cells.

Conclusions

PFAA patterns in cancer patients are altered perioperatively. Tumor-related amino acids identified by dynamic study of PFAA patterns may have the potential to be developed as novel biomarkers for diagnosis and prognosis of cancer patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0408-1) contains supplementary material, which is available to authorized users.

Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery

Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics.

Metabolomics in cell culture--a strategy to study crucial metabolic pathways in cancer development and the response to treatment

Metabolomics is a comprehensive tool for monitoring processes within biological systems. Thus, metabolomics may be widely applied to the determination of diagnostic biomarkers for certain diseases or treatment outcomes. There is significant potential for metabolomics to be implemented in cancer research because cancer may modify metabolic pathways in the whole organism. However, not all biological questions can be answered solely by the examination of small molecule composition in biofluids; in particular, the study of cellular processes or preclinical drug testing requires ex vivo models. The major objective of this review was to summarise the current achievement in the field of metabolomics in cancer cell culture-focusing on the metabolic pathways regulated in different cancer cell lines-and progress that has been made in the area of drug screening and development by the implementation of metabolomics in cell lines.